Neural Networks Workshop
Apr
13
6:30 PM18:30

Neural Networks Workshop

Come out to UF DSI's Introduction to Neural Networks! Neural networks are some of the most powerful machine learning algorithms available today, with applications in computer vision, speech recognition, pattern classification, and many more.

The first half of the workshop will cover the theory behind neural networks. We will focus on the structure, backpropagation of error, and training of feedforward neural networks.

In the second half, we will delve into how to construct a simple feedforward network using Python's class system. We will train this network on a set of training data and evaluate its performance. Finally, we will take a brief look at some advanced neural network architectures.

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Python 2 Workshop
Apr
10
6:30 PM18:30

Python 2 Workshop

Come out to the Python 2 Workshop! This is the last workshop in the Python series! Python 2 will cover an introduction to Machine Learning concepts in Python. Machine learning is quickly becoming one of the most sought-after skill sets in the technology industry, as it allows computers to recognize patterns and make predictions without being explicitly programmed to do so.
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To prepare for the upcoming workshop, please follow the steps below to download the programs and we’ll teach you the rest! (There's no need to do this if you have already attended a DSI workshop and have done it.)

Download and install Anaconda Python distribution for PYTHON 2. WE ARE USING PYTHON 2, NOT PYTHON 3. (this includes Jupyter, a Python interpreter that will allow you to run iPython notebooks)

Use this link:
https://www.continuum.io/downloads

Download the iPython notebooks and files from GitHub. Just click “clone or download" in the top right-hand corner, and select "download zip"

Use this link: 
https://github.com/dsiufl/Python-Workshops

Open up the Anaconda launcher, the Jupyter page will open in a web browser, and navigate to the location of the downloaded files. Through the Jupyter webpage, you will be able to run the iPython inotebook.

The workshop that we will be using is Python II Fall 2016 - Student.ipynb.

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Lake Wauburg Social
Apr
8
10:00 AM10:00

Lake Wauburg Social

Hello DSI members! Come out to our Lake Wauburg social this Saturday! We will be meeting on the North Shore of Lake Wauburg.

 

To get to Lake Wauburg, you can take the bus route 128 from the Reitz Union bus stop. Please remember we will be at the NORTH SHORE, not the south shore.

 

We also have people that can give rides. If you need a ride to the lake or you can provide a ride, please post on this event.

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Data API Workshop
Apr
6
6:30 PM18:30

Data API Workshop

Data science doesn’t work without data! Getting access to and formatting data often the most difficult and time comsuming part of an analysis. Open APIs make data access simpler and faster and understanding how they work will make a world of data availible to you. This workshop uses Python to explore a range of typical data APIs. 

To prepare, please follow the steps below to download the programs and we’ll teach you the rest!

Download and install Anaconda Python distribution for PYTHON 2. WE ARE USING PYTHON 2, NOT PYTHON 3. (this includes Jupyter, a Python interpreter that will allow you to run iPython notebooks) Use this link:https://www.continuum.io/

Download the DataAPIs-Workshop notebooks and files from GitHub. Just click “clone or download” in the top right-hand corner, and select “download zip” Use this link: https://github.com/dsiufl/DataAPIs-Workshop

Open up the Anaconda launcher, the Jupyter page will open in a web browser, and navigate to the location of the downloaded files. Through the Jupyter webpage, you will be able to run the iPython inotebook.

You will also need some additional Python packages. More details about how to install these in Anaconda are coming.

1. census
2. basemap
3. geocoder

Some APIs require API keys (a text string that is issued to you so they can monitor and restrict your usage) which you will need to also obtain before the workshop.

You will need to visit these links to get an API key for the US Census data and for the Google mapping API. Both are free.

1. US Census API key request : http://api.census.gov/data/key_signup.html
2. Google Mapping API key request You should be able to just click the “GET A KEY” button, you don’t need to do the 6-step process. : https://developers.google.com/maps/documentation/geocoding/get-api-key#get-an-api-key

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Python 1 Workshop
Apr
3
6:30 PM18:30

Python 1 Workshop

Python 1 is structured as an introduction to data anaylsis with Python. In this workshop, you will learn how to upload .csv files, clean your data, and perform some basic analytics and visualization. This workshop will introduce you to the numpy and pandas python libraries, which are used for scientific computing and databases, resepctively.

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To prepare for the upcoming workshop, please follow the steps below to download the programs and we’ll teach you the rest! (There's no need to do this if you have already attended a DSI workshop and have done it.)


STEP ONE IF NOT ALREADY DONE:
Download and install Anaconda Python distribution for PYTHON 2. WE ARE USING PYTHON 2, NOT PYTHON 3. (this includes Jupyter, a Python interpreter that will allow you to run iPython notebooks)

1)Use this link:
https://www.continuum.io/downloads


STEP TWO IF NOT ALREADY DONE
Use this link: 
https://github.com/dsiufl/Python-Workshops

Download the iPython notebooks and files from GitHub. Just click “clone or download" in the top right-hand corner, and select "download zip" then once it finishes, unzip it.



Open up the Anaconda launcher, the Jupyter page will open in a web browser, and navigate to the location of the downloaded files. Through the Jupyter webpage, you will be able to run the iPython inotebook.

The workshop that we will be using is: "DataSciUF Python I.ipynb."

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Data Visualization Workshop
Mar
30
6:30 PM18:30

Data Visualization Workshop

Deepen your Python skills by taking a tour of the visualization library Seaborn. Learn how to make stunning heatmaps, histograms and more.

To prepare for the upcoming workshop, please follow the steps below to download the programs and we’ll teach you the rest!

Download and install Anaconda Python distribution for PYTHON 2. WE ARE USING PYTHON 2, NOT PYTHON 3. (this includes Jupyter, a Python interpreter that will allow you to run iPython notebooks)

Use this link:
https://www.continuum.io/

Download the iPython notebooks and files from GitHub. Just click “clone or download" in the top right-hand corner, and select "download zip"

Use this link: 
https://github.com/dsiufl/DataViz

Open up the Anaconda launcher, the Jupyter page will open in a web browser, and navigate to the location of the downloaded files. Through the Jupyter webpage, you will be able to run the Jupyter inotebook.

We will be using "Data Visualization - Student.ipynb" for this workshop!

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Communication-Avoiding Statistical Inference
Mar
30
2:30 PM14:30

Communication-Avoiding Statistical Inference

Dr. MICHAEL JORDON

Pehong Chen Distinguished Professor
Departments of EECS and Statistics, AMP Lab, Berkeley AL Research Lab
University of California at Berkeley

Overview

The Department of Statistics at the University of Florida is pleased to announce that the 2016-2017 Challis Lectures will be given by Michael Jordan of the University of California, Berkeley. This year the Challis Lectures will be given in the Chamber on the ground floor of the Reitz Union (REI). Refreshments will be served 30 minutes beforehand in room REI G315. The first of the two Challis lectures is usually aimed at a broader scientific audience, while the second lecture may be more technical and specialized.

Thursday, March 30, 2017, 2:30-3:30PM

Communication-Avoiding Statistical Inference

Modern data analysis increasingly takes place on distributed computing platforms. In the distributed setting, procedures that minimize communication among processors can be orders-of-magnitude faster than naive procedures. This fact has revolutionized numerical linear algebra, but it has yet to have significant impact on statistics. I discuss communication-avoiding approaches to statistical inference, including a novel form of the bootstrap, a primal-dual approach to M-estimation, a surrogate likelihood framework and distributed forms of false discovery rate control. 

Location

Chamber Room (on ground floor), Reitz Union

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Media Archaeology, from Interface to Undersea Cable
Mar
30
11:30 AM11:30

Media Archaeology, from Interface to Undersea Cable

RSVP Here: https://informatics.institute.ufl.edu/event/media-archaeology-from-interface-to-undersea-cable/

Dr. Lori Emerson

Director of Media Archaeology Lab
Associate Professor of English and Intermedia Art, Writing and Performance
University of Colorado at Boulder

Abstract

Lori Emerson will discuss the past, present and philosophy of the Media Archaeology Lab as well as the connections between the lab, the field of media archaeology, and her earlier work on interfaces along with her present work on pre-Internet artist networks.

Bio

Lori Emerson is an Associate Professor in the Department of English and the Intermedia Arts, Writing, and Performance Program at the University of Colorado at Boulder. She is also Founding Director of the Media Archaeology Lab. Emerson writes about media poetics as well as the history of computing, media archaeology, media theory, and digital humanities. She is currently working on two book projects: the first is called “Other Networks” and is a history of telecommunications networks that existed before or outside of the Internet; the second is called “THE LAB BOOK: Situated Practices in Media Studies” (under contract with the University of Minnesota Press) which she is co-writing with Jussi Parikka and Darren Wershler. Emerson is the author of Reading Writing Interfaces: From the Digital to the Bookbound (University of Minnesota Press, June 2014). She is also co-editor of three collections: The Johns Hopkins Guide to Digital Media, with Marie-Laure Ryan and Benjamin Robertson (2014); Writing Surfaces: The Selected Fiction of John Riddell, with Derek Beaulieu (Wilfred Laurier University Press, 2013); and The Alphabet Game: a bpNichol Reader, with Darren Wershler (Coach House Books 2007).

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On Computational Thinking, Inferential Thinking and Data Science
Mar
29
3:30 PM15:30

On Computational Thinking, Inferential Thinking and Data Science

Dr. MICHAEL JORDON

Pehong Chen Distinguished Professor
Departments of EECS and Statistics, AMP Lab, Berkeley AL Research Lab
University of California at Berkeley

Overview

The Department of Statistics at the University of Florida is pleased to announce that the 2016-2017 Challis Lectures will be given by Michael Jordan of the University of California, Berkeley. This year the Challis Lectures will be given in the Chamber on the ground floor of the Reitz Union (REI). Refreshments will be served 30 minutes beforehand in room REI G315. The first of the two Challis lectures is usually aimed at a broader scientific audience, while the second lecture may be more technical and specialized.

Wednesday, March 29, 2017, 3:30-4:30PM

On Computational Thinking, Inferential Thinking and Data Science

The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in “Big Data” is apparent from their sharply divergent nature at an elementary level—in computer science, the growth of the number of data points is a source of “complexity” that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of “simplicity” in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as “runtime” in core statistical theory and the lack of a role for statistical concepts such as “risk” in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and methods for trading off the speed and accuracy of inference.

Location

Chamber Room (on ground floor), Reitz Union

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Identifying Phase Space Boundaries with Voronoi Tessellations
Mar
29
12:00 PM12:00

Identifying Phase Space Boundaries with Voronoi Tessellations

Abstract

Determining the masses of new physics particles appearing in decay chains is an important and longstanding problem in high energy phenomenology. Recently it has been shown that these mass measurements can be improved by utilizing the boundary of the allowed region in the fully dierentiable phase space in its full dimensionality. Here we show that the practical challenge of identifying this boundary can be solved using techniques based on the geometric properties of the cells resulting from Voronoi tessellations of the relevant data. The robust detection of such phase space boundaries in the data could also be used to corroborate a new physics discovery based on a cut-and-count analysis.

Bio

Dipsikha Debnath is a graduate student in the Department of Physics and an UF II fellow. She works to develop new analysis techniques that will translate the large data sets describing particle collisions at the CERN Large Hadron Collider (LHC) into discoveries that will transform our understanding of the fundamental laws that govern the universe. In this she takes an interdisciplinary approach, combining techniques from statistics and applied mathematics, in particular computational geometry, to provide tools for ground-breaking discoveries in elementary particle physics. The LHC is the world’s most powerful particle collider, which produces incredibly large amounts of data – millions of proton-proton collisions every second. The LHC detectors record only a tiny fraction of these collisions, yet this still amounts to 15 petabytes (15,000,000 gigabytes) of collected data every year. Each recorded event is quite complex, and contains hundreds of particles in the final state. As a “collider phenomenologist”, i.e., a particle theorist who studies new physics signatures at colliders, she uses intensive computational simulation and design complex data analysis techniques that makes it possible to learn fundamental lessons about the nature of our physical world and universe at the LHC. Dipsikha’s homepage: http://www.phys.ufl.edu/~debnath and Inspire profile: http://inspirehep.net/author/profile/D.Debnath.2.

Lunch will be available prior to talk (11:30AM)

Location

Fellows Journal Club meet in E252 CSE Building

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Python 0 Workshop
Mar
27
6:30 PM18:30

Python 0 Workshop

This workshop will walk you through the essentials of programming in the Python language, with an emphasis on data analysis. This workshop is beginner friendly, and all skill levels are encouraged to attend. Please bring a laptop. RSVP through our Facebook event.

To prepare for the upcoming workshop, please follow the steps below to download the programs and we’ll teach you the rest!

Download and install Anaconda Python distribution for PYTHON 2. WE ARE USING PYTHON 2, NOT PYTHON 3. (this includes Jupyter, a Python interpreter that will allow you to run iPython notebooks)

Use this link:
https://www.continuum.io/downloads

Download the iPython notebooks and files from GitHub. Just click “clone or download" in the top right-hand corner, and select "download zip"

Use this link: 
https://github.com/dsiufl/Python-Workshops

Open up the Anaconda launcher, the Jupyter page will open in a web browser, and navigate to the location of the downloaded files. Through the Jupyter webpage, you will be able to run the iPython inotebook.

The workshop that we will be using is UF DSI Python 0 - 10_26_16 - Student.ipynb.

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Kaggle 2 Workshop
Mar
24
6:30 PM18:30

Kaggle 2 Workshop

This workshop is a continuation of the first, involving the continuation of the Titanic problem.

https://github.com/dsiufl/Kaggle-Workshop

Kaggle is a data science competition website where people can compete with others by solving problems.

Come out to our Kaggle workshop covering an overview of Kaggle, including registration, its competitions, kernels, forums & Jobs. 

We will also continue our overview of the Titanic problem to introduce a pipeline of data science, including Data preparation, Data cleaning & Analysis & visualization, machine learning techniques, some stories about titanic/people, and how to submit a solution. 

If you are interested in Kaggle, feel free to join us!

Here is the anaconda distribution to download in order to run the notebook: (Download Python 2.7)

https://www.continuum.io/downloads


Here are the github files for the workshop:

https://github.com/dsiufl/Kaggle-Workshop

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Spark Workshop
Mar
20
6:30 PM18:30

Spark Workshop

In this workshop we explain what Spark is and how it works. Then we will describe the basics of running Spark on your computer and programming for it through an iPython notebook. We will write some Python to perform text mining with Spark on a small dataset. At the end of the workshop we will demonstrate running the same code distributed across a cluster with a much larger dataset to show how Spark parallelizes and distributes computation.

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DSI Symposium
Mar
18
9:35 AM09:35

DSI Symposium

Come spend your Saturday at the largest DSI event of the year - our annual Symposium. 

Begin the day with Einstein's bagels for breakfast, remarks from DSI leadership and the UFII Director, and a keynote by Jack Kendall, the founder of a Deep Learning startup here in Gainesville. 

The symposium continues with speakers from a wide range of research fields at UF iin three breakout sessions of four speakers each. Learn about computer vision, bioinformatics, political forecasting, business analytics, and more.

Our symposium will also include two rounds of workshops with several choices in each round- so you can brush up on your Python, learn about data visualization, or deepen your knowledge of machine learning.

This is a fantastic opportunity to network with students and faculty who are passionate about the impact of data science and the tools they utilize to realize that impact. 

Breakfast and Lunch will be served. 

If you plan to attend, please RSVP through this form: 

https://goo.gl/forms/p6OJ9ppLLfmumfXK2

While we urge you to RSVP for food estimates, we will not turn anyone away, so feel free to bring a friend! 


The schedule is below: 

9:45 - 10:30 Registration & Breakfast Opens in Grand Ballroom

10:30 - 10:45 Introduction by DSI Leadership - Gordon Wilson, DSI President
10:45 - 11:00 Dr. George Michailidis, UFII Director
11:00 - 11:25 Keynote: Jack Kendall - Designing Neuromorphic Hardware for Machine Learning
11:30 - 11:55 Breakout Session 1 (20 minute presentations, 5 minute Q&A)
12:00 - 12:25 Breakout Session 2 (20 minute presentations, 5 minute Q&A)
12:30 - 12:55 Breakout Session 3 (20 minute presentations, 5 minute Q&A)
1:00 - 2:00 Lunch
2:00 - 2:45 Workshop Session 1
2:50 - 3:40 Workshop Session 2
3:40 - 4:00 Closing Remarks and How to get involved

We will be sending a full schedule with speakers and workshops shortly!

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R1 Workshop
Feb
23
6:30 PM18:30

R1 Workshop

In this R workshop we will finish up some topics left over from R0 and take a look into importing, cleaning and manipulating data sets from various file types and locations. 

TO DOWNLOAD RSTUDIO:

First download R: https://cran.cnr.berkeley.edu/ 

For Windows: Open up the link for windows and select ‘install R for the first time’

For Mac: Open up the link for Mac OS X and select ‘R-3.2.4.pkg’

Once you have downloaded R we need to download R-studio
https://www.rstudio.com/products/rstudio/download/

Select your operating system and complete the download. 

Run Rstudio and make sure you can run something like ‘1+1’

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Kaggle Workshop
Feb
22
6:30 PM18:30

Kaggle Workshop

Kaggle is a data science competition website where people can compete with others by solving problems.

Come out to our Kaggle workshop covering an overview of Kaggle, including registration, its competitions, kernels, forums & Jobs. 

We will also give an overview of the Titanic problem to introduce a pipeline of data science, including
Data preparation, Data cleaning & Analysis & visualization, machine learning techniques, some stories about titanic/people, and how to submit a solution. 

If you are interested in Kaggle, feel free to join us!

Here is the anaconda distribution to download in order to run the notebook: (Download Python 2.7)

https://www.continuum.io/downloads


Here are the github files for the workshop:

https://github.com/dsiufl/Kaggle-Workshop

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